def test_mnist_classifier(self):
# Given
training_set, test_set = _load_data('../data/mnist.pkl.gz')
network_specification = [
InputLayer([28, 28]),
LeNetConvPoolLayer(feature_map=2,
filter_shape=(5, 5),
pool_size=(2, 2)),
HiddenLayer(50),
LogisticRegressionLayer(10)
]
neural_network = MultiLayerPerceptron(
seed=1234, network_specification=network_specification)
# When
neural_network.train(training_set=training_set,
learning_rate=0.1,
batch_size=500,
iterations=1)
# Then
self.assertEqual(
28.18,
round(neural_network.test(test_set=test_set, batch_size=1000), 2))
def le_cun_minst_classifier(data_set='../data/mnist.pkl.gz'):
# Prepare data
training_set, test_set = load_data(data_set)
# Create network
network_specification = [
InputLayer([28, 28]),
LeNetConvPoolLayer(feature_map=20,
filter_shape=(5, 5),
pool_size=(2, 2)),
LeNetConvPoolLayer(feature_map=50,
filter_shape=(5, 5),
pool_size=(2, 2)),
HiddenLayer(500),
LogisticRegressionLayer(10)
]
neural_network = MultiLayerPerceptron(
seed=1234, network_specification=network_specification)
# Train
neural_network.train(training_set=training_set,
learning_rate=0.1,
batch_size=100,
iterations=50)
# Test
print "Error rate of {}%".format(
neural_network.test(test_set=test_set, batch_size=1000))
def simple_mnist_classification(data_set='../data/mnist.pkl.gz'):
# Prepare data
training_set, test_set = load_data(data_set)
# Create network
network_specification = [
InputLayer([784]),
HiddenLayer(784),
HiddenLayer(400),
HiddenLayer(200),
HiddenLayer(100),
HiddenLayer(50),
HiddenLayer(25),
LogisticRegressionLayer(10)
]
neural_network = MultiLayerPerceptron(
seed=1234, network_specification=network_specification)
# Train
neural_network.train(training_set=training_set,
learning_rate=0.1,
batch_size=100,
iterations=50)
# Test
print "Error rate of {}%".format(
neural_network.test(test_set=test_set, batch_size=1000))
def test_XOR_problem_classification(self):
# Given
network_specification = [
InputLayer([2]),
HiddenLayer(4),
LogisticRegressionLayer(2)
]
training_set = np.asarray([[[0.0, 0.0], 0], [[0.0, 1.0], 1],
[[1.0, 0.0], 1], [[1.0, 1.0], 0]])
multilayer_perceptron_classifier = MultiLayerPerceptron(
seed=1234, network_specification=network_specification)
# When
multilayer_perceptron_classifier.train(training_set,
iterations=100,
learning_rate=0.1)
# Then
self.assertEqual(
0, multilayer_perceptron_classifier.predict([[0.0, 0.0]]))
self.assertEqual(
1, multilayer_perceptron_classifier.predict([[0.0, 1.0]]))
self.assertEqual(
1, multilayer_perceptron_classifier.predict([[1.0, 0.0]]))
self.assertEqual(
0, multilayer_perceptron_classifier.predict([[0.0, 0.0]]))
self.assertTrue(
multilayer_perceptron_classifier.test(training_set) == 0)
def test_XOR_problem_regression(self):
# Given
network_specification = [
InputLayer([2]),
HiddenLayer(2),
LinearRegressionLayer(1)
]
training_set = np.asarray([[[0.0, 0.0], [0.0]], [[0.0, 1.0], [1.0]],
[[1.0, 0.0], [1.0]], [[1.0, 1.0], [0.0]]])
multilayer_perceptron_regressor = MultiLayerPerceptron(
seed=1234, network_specification=network_specification)
# When
multilayer_perceptron_regressor.train(training_set,
iterations=1000,
learning_rate=0.1)
# Then
self.assertTrue(
multilayer_perceptron_regressor.predict([[0, 0]])[0] < 0.0001)
self.assertTrue(
multilayer_perceptron_regressor.predict([[0, 1]])[0] > 0.9999)
self.assertTrue(
multilayer_perceptron_regressor.predict([[1, 0]])[0] > 0.9999)
self.assertTrue(
multilayer_perceptron_regressor.predict([[1, 1]])[0] < 0.0001)
self.assertTrue(
multilayer_perceptron_regressor.test(training_set) < 0.0001)
def xor_regression():
network_specification = [
InputLayer([2]),
HiddenLayer(4),
LogisticRegressionLayer(2)
]
training_set = np.asarray([[[0.0, 0.0], 0], [[0.0, 1.0], 1], [[1.0, 0.0],
1],
[[1.0, 1.0], 0]])
multilayer_perceptron_regressor = MultiLayerPerceptron(
seed=1234, network_specification=network_specification)
multilayer_perceptron_regressor.train(training_set,
iterations=1000,
learning_rate=0.1)
print "0:0 leads to: " + str(
multilayer_perceptron_regressor.predict([[0, 0]])[0])
print "1:0 leads to: " + str(
multilayer_perceptron_regressor.predict([[1, 0]])[0])
print "0:1 leads to: " + str(
multilayer_perceptron_regressor.predict([[0, 1]])[0])
print "1:1 leads to: " + str(
multilayer_perceptron_regressor.predict([[1, 1]])[0])
def test_invalid_output_size_regressor(self):
# Given
training_set = np.array([[[1, 1], [2]]])
network_specification = [
InputLayer([2]),
HiddenLayer(2),
LinearRegressionLayer(2)
]
multilayer_perceptron_regressor = MultiLayerPerceptron(
seed=1234, network_specification=network_specification)
# Then
self.assertRaises(InvalidDimensionError,
multilayer_perceptron_regressor.train, training_set)
def test_no_numpy_array(self):
# Given
training_set = [[[1, 2, 3, 4], [1, 2]]]
multilayer_perceptron_regressor = MultiLayerPerceptron(
seed=1234,
network_specification=[
InputLayer([4]),
HiddenLayer(2),
LinearRegressionLayer(2)
])
# Then
self.assertRaises(NoNumpyArrayError,
multilayer_perceptron_regressor.train, training_set)
def test_missing_data_set(self):
# Given
training_set = []
multilayer_perceptron_regressor = MultiLayerPerceptron(
seed=1234,
network_specification=[
InputLayer([1]),
HiddenLayer(2),
LinearRegressionLayer(1)
])
# Then
self.assertRaises(NoDataSetFoundError,
multilayer_perceptron_regressor.train, training_set)
def test_invalid_data_set_format(self):
# Given
training_set = [[[1]]]
multilayer_perceptron_regressor = MultiLayerPerceptron(
seed=1234,
network_specification=[
InputLayer([1]),
HiddenLayer(2),
LinearRegressionLayer(1)
])
# Then
self.assertRaises(InvalidDataError,
multilayer_perceptron_regressor.train, training_set)
def test_network_initialization(self):
# Given
network_specification = [
InputLayer([4]),
HiddenLayer(3),
LinearRegressionLayer(2)
]
# When
multilayer_perceptron_regressor = MultiLayerPerceptron(
seed=1234, network_specification=network_specification)
# Then
self.assertEqual(
1,
len(multilayer_perceptron_regressor._network_specification[1:-1]))
self.assertEqual(
3, multilayer_perceptron_regressor._network_specification[1].size)
self.assertEqual(2, multilayer_perceptron_regressor._output_layer.size)